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Average conditional correlation and tree structures for multivariate GARCH models
Author(s) -
Audrino Francesco,
BaroneAdesi Giovanni
Publication year - 2006
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1014
Subject(s) - autoregressive conditional heteroskedasticity , multivariate statistics , conditional variance , econometrics , series (stratigraphy) , mathematics , benchmark (surveying) , statistics , variance (accounting) , computer science , economics , volatility (finance) , paleontology , accounting , geodesy , biology , geography
We propose a simple class of multivariate GARCH models, allowing for time‐varying conditional correlations. Estimates for time‐varying conditional correlations are constructed by means of a convex combination of averaged correlations (across all series) and dynamic realized (historical) correlations. Our model is very parsimonious. Estimation is computationally feasible in very large dimensions without resorting to any variance reduction technique. We back‐test the models on a six‐dimensional exchange‐rate time series using different goodness‐of‐fit criteria and statistical tests. We collect empirical evidence of their strong predictive power, also in comparison to alternative benchmark procedures. Copyright © 2006 John Wiley & Sons, Ltd.